{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0.03921569,  0.03529412,  0.02352941,  1.        ],\n",
       "        [ 0.25098041,  0.1882353 ,  0.20392157,  1.        ],\n",
       "        [ 0.41176471,  0.34117648,  0.37254903,  1.        ],\n",
       "        ..., \n",
       "        [ 0.20392157,  0.23529412,  0.17254902,  1.        ],\n",
       "        [ 0.16470589,  0.18039216,  0.12156863,  1.        ],\n",
       "        [ 0.18039216,  0.18039216,  0.14117648,  1.        ]],\n",
       "\n",
       "       [[ 0.1254902 ,  0.11372549,  0.09411765,  1.        ],\n",
       "        [ 0.29019609,  0.25098041,  0.24705882,  1.        ],\n",
       "        [ 0.21176471,  0.2       ,  0.20392157,  1.        ],\n",
       "        ..., \n",
       "        [ 0.17647059,  0.24705882,  0.12156863,  1.        ],\n",
       "        [ 0.10980392,  0.15686275,  0.07843138,  1.        ],\n",
       "        [ 0.16470589,  0.20784314,  0.11764706,  1.        ]],\n",
       "\n",
       "       [[ 0.14117648,  0.12941177,  0.10980392,  1.        ],\n",
       "        [ 0.21176471,  0.1882353 ,  0.16862746,  1.        ],\n",
       "        [ 0.14117648,  0.13725491,  0.12941177,  1.        ],\n",
       "        ..., \n",
       "        [ 0.10980392,  0.15686275,  0.08627451,  1.        ],\n",
       "        [ 0.0627451 ,  0.08235294,  0.05098039,  1.        ],\n",
       "        [ 0.14117648,  0.2       ,  0.09803922,  1.        ]],\n",
       "\n",
       "       ..., \n",
       "       [[ 0.32156864,  0.30980393,  0.30980393,  1.        ],\n",
       "        [ 0.35294119,  0.27843139,  0.29019609,  1.        ],\n",
       "        [ 0.47843137,  0.40000001,  0.43137255,  1.        ],\n",
       "        ..., \n",
       "        [ 0.03921569,  0.03921569,  0.03529412,  1.        ],\n",
       "        [ 0.0627451 ,  0.05882353,  0.06666667,  1.        ],\n",
       "        [ 0.1254902 ,  0.12156863,  0.13333334,  1.        ]],\n",
       "\n",
       "       [[ 0.3137255 ,  0.25882354,  0.23921569,  1.        ],\n",
       "        [ 0.35294119,  0.24313726,  0.20392157,  1.        ],\n",
       "        [ 0.42352942,  0.3019608 ,  0.28235295,  1.        ],\n",
       "        ..., \n",
       "        [ 0.05490196,  0.05490196,  0.05490196,  1.        ],\n",
       "        [ 0.07058824,  0.06666667,  0.05882353,  1.        ],\n",
       "        [ 0.10196079,  0.08627451,  0.09019608,  1.        ]],\n",
       "\n",
       "       [[ 0.28627452,  0.2       ,  0.18039216,  1.        ],\n",
       "        [ 0.3137255 ,  0.22352941,  0.19215687,  1.        ],\n",
       "        [ 0.29411766,  0.2       ,  0.18431373,  1.        ],\n",
       "        ..., \n",
       "        [ 0.05882353,  0.05882353,  0.04705882,  1.        ],\n",
       "        [ 0.05882353,  0.0627451 ,  0.05490196,  1.        ],\n",
       "        [ 0.07843138,  0.08235294,  0.07058824,  1.        ]]], dtype=float32)"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from matplotlib.pyplot import imread\n",
    "imread(\"mushroom-small.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAARsAAAEYCAYAAABsuVKPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3524f95f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAARsAAAEYCAYAAABsuVKPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nFf4bLf+k7E+hOy4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd351fe6dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAARsAAAEYCAYAAABsuVKPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nFUo2EbFCySYiVijZRMQKJZuIWOG/AXkMzxWRys+XAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3525cc0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rgb = imread(\"mushroom-small.png\")\n",
    "\n",
    "for i in range(3):\n",
    "    tmp = np.zeros((50, 50, 3))\n",
    "    tmp[:, :, i] = rgb[:, :, i]\n",
    "    plt.imshow(tmp)\n",
    "    plt.axis(\"off\")\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(\"mushroom-rgb%d.png\" % i)\n",
    "    plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
